This disclosure relates to a data-driven method and system for estimating and tracking accurate operational states of mechanical systems. In particular, the disclosure relates to a data-driven method and system for estimating and tracking operational states, such as wear or anomalies over time, of mechanical systems.
Mechanical systems, such as engines, turbines, tires, brakes, and other system components, found in aircraft, automobiles, trucks, watercraft, power generator units, military vehicles, and other vehicles, wear or change over time. Wear affects the performance of such mechanical systems. A key factor in monitoring the health of a mechanical system is to measure system wear as it occurs over time. Such monitoring can aid in maintenance planning and timely repair or replacement of the mechanical system or components thereof. For example, with gas turbine engines, to get the same thrust output as an engine wears, the engine requires more fuel, and the engine's exhaust gas temperature (EGT), as it leaves the engine, increases. However, EGT is also affected by outside variables, such as environmental influences (e.g., temperature and air quality), flight conditions, system faults, and other engine parameters for any given flight or data point. Such factors may overwhelm the EGT value more than wear for a given data point. Typically, engine wear is not evident in a time series plot of raw EGT data plotted over the lifetime of an engine. Thus, EGT by itself may not reveal engine wear that is hidden by the variability due to environmental, operational and other factors.
Known methods and systems exist for monitoring and estimating the wear of a mechanical system. Empirical methods and systems for estimating wear typically manually manipulate recorded data into tables for lookup. Such manual empirical methods are limited in the amount of data that can be assembled and are not in an automated format to create a prediction model. In addition, such manual methods may be imprecise because of the outside influences discussed above. Other known methods and systems use theoretical models of the mechanical system which use physics or engineering information to build a model using test data. Such modeling is based on understanding how a system operates and progresses to a failure via knowledge, for example, of material properties and response to loading. However, such physics or engineering model-based methods may use simplifying assumptions and are theoretical in nature. Moreover, such methods and systems only collect data when the engine is new and do not continually collect data during flights or track degradation of a system over time. Outside influences, as in the empirical method, may not be accounted for.
With regard to known methods and systems that estimate mechanical system wear over time, the resulting output might be plotted over time to observe trends. The scatter of the individual points is large enough that large rolling averages are required to obtain a value that can be used with confidence. This can cause time delays for any corrective action that may be needed and also for prediction of scheduled maintenance for the engine.
Data collected over a system's life can be input to statistical learning models to estimate and track wear/change in a mechanical system. U.S. Patent Application Publ. No. 2010/0082267 (incorporated by reference herein) discloses an automated data-driven method for estimating one or more operational states, such as wear or degradation, of a mechanical system over time. The method comprises training a regression model at a fixed point of wear, and then applying it independently at time points over the life of the system to estimate wear. More specifically, the method comprises the steps of collecting data on the mechanical system from a data recording device, preprocessing the collected data, selecting a training data set that represents a base condition for statistical comparison, fitting a statistical model to the training data set to relate a system output to variables at the base condition, and using an output model to predict what an observed response would have been at the base condition and calculating the difference between the observed response and the predicted response to estimate the one or more operational states of the mechanical system. In particular, U.S. Patent Application Publ. No. 2010/0082267 discloses a procedure that empirically relates mechanical system output (e.g., engine EGT) to other factors (e.g., environmental, flight and mechanical parameters). The residuals are the difference between the observed mechanical system output (e.g., engine EGT) and the output predicted by the model, and represent mechanical system wear over time or operational anomaly (part failure).
As a baseline for comparison, an aircraft owner is typically provided normalized EGT data schedules by the engine manufacturer (for brevity, “OEM”). A previous investigation described by Basu et al. [see “Statistical Methods for Modeling and Predicting Maximum Engine Exhaust Gas Temperature (EGT): First Analysis Using Climb Data from a Single Aircraft”, Networked Systems Technology Technical Report (NST-08-001) (2008) and “Regression Based Method for Predicting Engine Wear from Exhaust Gas Temperature”, Prognostics and Health Management Conference, Denver, Colo. (2008)] showed that a data-driven approach outperformed the OEM results in the sense that its predictions (using a random forest) had a similar range for estimating engine wear, but about 25% smaller variation.
There is a need for a data-driven method and system that further reduces variability in estimation for operational states such as wear, and also monitors for more abrupt changes in the condition of mechanical systems.